Overview

Dataset statistics

Number of variables37
Number of observations440
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory127.3 KiB
Average record size in memory296.3 B

Variable types

NUM19
CAT17
BOOL1

Warnings

SpatDist has constant value "440" Constant
SpatGL has constant value "440" Constant
Bes2 has constant value "440" Constant
StrklVu has constant value "440" Constant
Lich2 is highly correlated with Lich1High correlation
Lich1 is highly correlated with Lich2High correlation
Month is highly correlated with df_indexHigh correlation
df_index is highly correlated with MonthHigh correlation
df_index has unique values Unique
TempDist has 273 (62.0%) zeros Zeros
Strasse has 137 (31.1%) zeros Zeros
UArt1 has 25 (5.7%) zeros Zeros
AUrs1 has 406 (92.3%) zeros Zeros
Fstf has 200 (45.5%) zeros Zeros
Month has 30 (6.8%) zeros Zeros

Reproduction

Analysis started2020-10-27 07:52:07.762976
Analysis finished2020-10-27 07:53:13.811508
Duration1 minute and 6.05 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct440
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean912.9886364
Minimum0
Maximum1860
Zeros1
Zeros (%)0.2%
Memory size3.4 KiB
2020-10-27T08:53:14.188489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile81.9
Q1430
median906
Q31377
95-th percentile1763.25
Maximum1860
Range1860
Interquartile range (IQR)947

Descriptive statistics

Standard deviation541.0571213
Coefficient of variation (CV)0.5926219668
Kurtosis-1.182935883
Mean912.9886364
Median Absolute Deviation (MAD)474
Skewness0.02873321944
Sum401715
Variance292742.8085
MonotocityStrictly increasing
2020-10-27T08:53:14.486810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
64510.2%
 
32410.2%
 
132410.2%
 
30110.2%
 
167810.2%
 
30410.2%
 
133210.2%
 
133710.2%
 
31410.2%
 
31810.2%
 
Other values (430)43097.7%
 
ValueCountFrequency (%) 
010.2%
 
710.2%
 
1110.2%
 
1510.2%
 
1610.2%
 
ValueCountFrequency (%) 
186010.2%
 
185910.2%
 
185810.2%
 
185410.2%
 
184810.2%
 

TempMax
Real number (ℝ≥0)

Distinct143
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.5295455
Minimum9
Maximum1341
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2020-10-27T08:53:14.650990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile24
Q172
median132
Q3264
95-th percentile597.9
Maximum1341
Range1332
Interquartile range (IQR)192

Descriptive statistics

Standard deviation181.9775301
Coefficient of variation (CV)0.9354750182
Kurtosis5.479572665
Mean194.5295455
Median Absolute Deviation (MAD)78
Skewness1.99964178
Sum85593
Variance33115.82145
MonotocityNot monotonic
2020-10-27T08:53:14.803138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
84112.5%
 
39102.3%
 
7892.0%
 
11792.0%
 
4592.0%
 
7281.8%
 
12081.8%
 
26481.8%
 
8771.6%
 
8171.6%
 
Other values (133)35480.5%
 
ValueCountFrequency (%) 
920.5%
 
1220.5%
 
1520.5%
 
1871.6%
 
2151.1%
 
ValueCountFrequency (%) 
134110.2%
 
98710.2%
 
88220.5%
 
86710.2%
 
78910.2%
 

TempAvg
Real number (ℝ≥0)

Distinct159
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.27954545
Minimum3
Maximum920
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2020-10-27T08:53:14.965836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q130
median55
Q394.25
95-th percentile216.3
Maximum920
Range917
Interquartile range (IQR)64.25

Descriptive statistics

Standard deviation78.61675177
Coefficient of variation (CV)1.044330851
Kurtosis34.6503918
Mean75.27954545
Median Absolute Deviation (MAD)31
Skewness4.373476935
Sum33123
Variance6180.593658
MonotocityNot monotonic
2020-10-27T08:53:16.232220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1592.0%
 
4792.0%
 
3692.0%
 
2681.8%
 
1681.8%
 
3771.6%
 
5371.6%
 
4471.6%
 
2371.6%
 
6171.6%
 
Other values (149)36282.3%
 
ValueCountFrequency (%) 
310.2%
 
410.2%
 
540.9%
 
651.1%
 
740.9%
 
ValueCountFrequency (%) 
92010.2%
 
53310.2%
 
50210.2%
 
38810.2%
 
32410.2%
 

SpatMax
Real number (ℝ≥0)

Distinct404
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16118.08409
Minimum1036
Maximum219082
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2020-10-27T08:53:16.395543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1036
5-th percentile2100.8
Q16159
median10988.5
Q318798.75
95-th percentile36189
Maximum219082
Range218046
Interquartile range (IQR)12639.75

Descriptive statistics

Standard deviation24195.96693
Coefficient of variation (CV)1.501168923
Kurtosis42.16232329
Mean16118.08409
Median Absolute Deviation (MAD)5590.5
Skewness6.056043147
Sum7091957
Variance585444815.7
MonotocityNot monotonic
2020-10-27T08:53:16.537446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
347530.7%
 
3549030.7%
 
150330.7%
 
18973030.7%
 
1316330.7%
 
19531020.5%
 
1120920.5%
 
686920.5%
 
2947020.5%
 
709120.5%
 
Other values (394)41594.3%
 
ValueCountFrequency (%) 
103610.2%
 
116710.2%
 
121610.2%
 
127910.2%
 
131510.2%
 
ValueCountFrequency (%) 
21908210.2%
 
19531020.5%
 
18973030.7%
 
13578010.2%
 
6141110.2%
 

SpatAvg
Real number (ℝ≥0)

Distinct404
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4315.097727
Minimum135
Maximum17805
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2020-10-27T08:53:16.680188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1081.3
Q12020.75
median3656
Q35759.5
95-th percentile10182
Maximum17805
Range17670
Interquartile range (IQR)3738.75

Descriptive statistics

Standard deviation2974.153773
Coefficient of variation (CV)0.6892436652
Kurtosis1.934259732
Mean4315.097727
Median Absolute Deviation (MAD)1837
Skewness1.296958296
Sum1898643
Variance8845590.667
MonotocityNot monotonic
2020-10-27T08:53:17.176510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
141330.7%
 
1026630.7%
 
657530.7%
 
144530.7%
 
406630.7%
 
630820.5%
 
560620.5%
 
633220.5%
 
149220.5%
 
1018220.5%
 
Other values (394)41594.3%
 
ValueCountFrequency (%) 
13510.2%
 
35810.2%
 
38710.2%
 
39310.2%
 
54410.2%
 
ValueCountFrequency (%) 
1780510.2%
 
1685110.2%
 
1513210.2%
 
1478510.2%
 
1419810.2%
 

TempDist
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.209090909
Minimum0
Maximum24
Zeros273
Zeros (%)62.0%
Memory size3.4 KiB
2020-10-27T08:53:17.876472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile21
Maximum24
Range24
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.785815911
Coefficient of variation (CV)1.612180886
Kurtosis1.184728552
Mean4.209090909
Median Absolute Deviation (MAD)0
Skewness1.540150713
Sum1852
Variance46.04729758
MonotocityNot monotonic
2020-10-27T08:53:18.010216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
027362.0%
 
6133.0%
 
8133.0%
 
12112.5%
 
10112.5%
 
4102.3%
 
292.0%
 
592.0%
 
792.0%
 
992.0%
 
Other values (14)7316.6%
 
ValueCountFrequency (%) 
027362.0%
 
151.1%
 
292.0%
 
351.1%
 
4102.3%
 
ValueCountFrequency (%) 
2461.4%
 
2361.4%
 
2251.1%
 
2171.6%
 
2020.5%
 

SpatDist
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
0
440 
ValueCountFrequency (%) 
0440100.0%
 
2020-10-27T08:53:18.098825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Coverage
Real number (ℝ≥0)

Distinct90
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.53636364
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2020-10-27T08:53:19.929209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q120
median32
Q348.5
95-th percentile85
Maximum100
Range98
Interquartile range (IQR)28.5

Descriptive statistics

Standard deviation22.79438738
Coefficient of variation (CV)0.6072614706
Kurtosis0.12964919
Mean37.53636364
Median Absolute Deviation (MAD)13
Skewness0.9256154032
Sum16516
Variance519.5840961
MonotocityNot monotonic
2020-10-27T08:53:20.083592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
21163.6%
 
18153.4%
 
15143.2%
 
16133.0%
 
25133.0%
 
19122.7%
 
27112.5%
 
30112.5%
 
42112.5%
 
31102.3%
 
Other values (80)31471.4%
 
ValueCountFrequency (%) 
220.5%
 
320.5%
 
540.9%
 
610.2%
 
730.7%
 
ValueCountFrequency (%) 
10071.6%
 
9810.2%
 
9710.2%
 
9610.2%
 
9510.2%
 

TempGL
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
3
273 
1
143 
5
 
24
ValueCountFrequency (%) 
327362.0%
 
114332.5%
 
5245.5%
 
2020-10-27T08:53:20.516578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:53:21.077267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:25.596955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

SpatGL
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2
440 
ValueCountFrequency (%) 
2440100.0%
 
2020-10-27T08:53:27.587031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:53:30.462223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:30.533136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

TempIL
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
3
272 
-1
167 
2
 
1
ValueCountFrequency (%) 
327261.8%
 
-116738.0%
 
210.2%
 
2020-10-27T08:53:30.640700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2020-10-27T08:53:30.720707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:34.144322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.379545455
Min length1

SpatIL
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2
400 
1
 
40
ValueCountFrequency (%) 
240090.9%
 
1409.1%
 
2020-10-27T08:53:34.266599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:53:34.350791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:36.954219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

TLCar
Real number (ℝ≥0)

Distinct339
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1513.763636
Minimum1000
Maximum1999
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2020-10-27T08:53:37.075405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1057.8
Q11255
median1526
Q31749
95-th percentile1950.15
Maximum1999
Range999
Interquartile range (IQR)494

Descriptive statistics

Standard deviation288.6177624
Coefficient of variation (CV)0.1906623699
Kurtosis-1.208762545
Mean1513.763636
Median Absolute Deviation (MAD)236.5
Skewness-0.07980130732
Sum666056
Variance83300.2128
MonotocityNot monotonic
2020-10-27T08:53:37.231255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
167840.9%
 
121340.9%
 
129340.9%
 
147440.9%
 
195540.9%
 
129130.7%
 
115230.7%
 
119430.7%
 
162330.7%
 
169330.7%
 
Other values (329)40592.0%
 
ValueCountFrequency (%) 
100010.2%
 
100110.2%
 
100310.2%
 
100630.7%
 
100810.2%
 
ValueCountFrequency (%) 
199910.2%
 
199810.2%
 
198410.2%
 
198120.5%
 
198010.2%
 

TLHGV
Real number (ℝ≥0)

Distinct291
Distinct (%)66.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean749.6045455
Minimum501
Maximum999
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2020-10-27T08:53:37.375293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile532
Q1616.75
median744.5
Q3872.5
95-th percentile969.15
Maximum999
Range498
Interquartile range (IQR)255.75

Descriptive statistics

Standard deviation144.0702277
Coefficient of variation (CV)0.1921949761
Kurtosis-1.270523983
Mean749.6045455
Median Absolute Deviation (MAD)128
Skewness0.02042616191
Sum329826
Variance20756.2305
MonotocityNot monotonic
2020-10-27T08:53:37.522217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
57961.4%
 
79540.9%
 
56740.9%
 
62640.9%
 
92640.9%
 
86240.9%
 
58830.7%
 
89330.7%
 
56930.7%
 
66930.7%
 
Other values (281)40291.4%
 
ValueCountFrequency (%) 
50110.2%
 
50510.2%
 
50610.2%
 
50910.2%
 
51020.5%
 
ValueCountFrequency (%) 
99930.7%
 
99820.5%
 
99510.2%
 
99410.2%
 
99020.5%
 

Strasse
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.188636364
Minimum0
Maximum13
Zeros137
Zeros (%)31.1%
Memory size3.4 KiB
2020-10-27T08:53:37.651862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile8
Maximum13
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.129455614
Coefficient of variation (CV)0.9814401071
Kurtosis-0.4059611673
Mean3.188636364
Median Absolute Deviation (MAD)2
Skewness0.7614925153
Sum1403
Variance9.793492441
MonotocityNot monotonic
2020-10-27T08:53:38.423307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
013731.1%
 
29421.4%
 
3388.6%
 
8358.0%
 
6347.7%
 
7327.3%
 
5296.6%
 
1133.0%
 
11112.5%
 
4102.3%
 
Other values (4)71.6%
 
ValueCountFrequency (%) 
013731.1%
 
1133.0%
 
29421.4%
 
3388.6%
 
4102.3%
 
ValueCountFrequency (%) 
1310.2%
 
1210.2%
 
11112.5%
 
1040.9%
 
910.2%
 

Kat
Categorical

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
7
196 
3
185 
2
46 
1
 
13
ValueCountFrequency (%) 
719644.5%
 
318542.0%
 
24610.5%
 
1133.0%
 
2020-10-27T08:53:39.654591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:53:41.500050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:48.288158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Typ
Real number (ℝ≥0)

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.120454545
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2020-10-27T08:53:48.380077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median6
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.846387786
Coefficient of variation (CV)0.3605906018
Kurtosis0.4772600407
Mean5.120454545
Median Absolute Deviation (MAD)0
Skewness-1.427450374
Sum2253
Variance3.409147857
MonotocityNot monotonic
2020-10-27T08:53:48.477199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
630569.3%
 
15612.7%
 
34410.0%
 
7317.0%
 
520.5%
 
420.5%
 
ValueCountFrequency (%) 
15612.7%
 
34410.0%
 
420.5%
 
520.5%
 
630569.3%
 
ValueCountFrequency (%) 
7317.0%
 
630569.3%
 
520.5%
 
420.5%
 
34410.0%
 

Betei
Real number (ℝ≥0)

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.238636364
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2020-10-27T08:53:48.576616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7694970428
Coefficient of variation (CV)0.3437347196
Kurtosis3.508590688
Mean2.238636364
Median Absolute Deviation (MAD)0
Skewness1.491213129
Sum985
Variance0.5921256989
MonotocityNot monotonic
2020-10-27T08:53:49.744312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
230068.2%
 
37116.1%
 
1388.6%
 
4225.0%
 
581.8%
 
610.2%
 
ValueCountFrequency (%) 
1388.6%
 
230068.2%
 
37116.1%
 
4225.0%
 
581.8%
 
ValueCountFrequency (%) 
610.2%
 
581.8%
 
4225.0%
 
37116.1%
 
230068.2%
 

UArt1
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.268181818
Minimum0
Maximum9
Zeros25
Zeros (%)5.7%
Memory size3.4 KiB
2020-10-27T08:53:49.849205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q33
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.33540373
Coefficient of variation (CV)0.7145880676
Kurtosis0.5573487107
Mean3.268181818
Median Absolute Deviation (MAD)1
Skewness1.241949333
Sum1438
Variance5.454110582
MonotocityNot monotonic
2020-10-27T08:53:49.949690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
219243.6%
 
310423.6%
 
8347.7%
 
5347.7%
 
0255.7%
 
9214.8%
 
1163.6%
 
7112.5%
 
630.7%
 
ValueCountFrequency (%) 
0255.7%
 
1163.6%
 
219243.6%
 
310423.6%
 
5347.7%
 
ValueCountFrequency (%) 
9214.8%
 
8347.7%
 
7112.5%
 
630.7%
 
5347.7%
 

UArt2
Real number (ℝ)

Distinct9
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2045454545
Minimum-1
Maximum9
Zeros2
Zeros (%)0.5%
Memory size3.4 KiB
2020-10-27T08:53:50.051163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.101763371
Coefficient of variation (CV)15.16417648
Kurtosis3.553495121
Mean0.2045454545
Median Absolute Deviation (MAD)0
Skewness2.317645315
Sum90
Variance9.620936012
MonotocityNot monotonic
2020-10-27T08:53:50.147187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
-137685.5%
 
9358.0%
 
8133.0%
 
371.6%
 
230.7%
 
720.5%
 
020.5%
 
510.2%
 
110.2%
 
ValueCountFrequency (%) 
-137685.5%
 
020.5%
 
110.2%
 
230.7%
 
371.6%
 
ValueCountFrequency (%) 
9358.0%
 
8133.0%
 
720.5%
 
510.2%
 
371.6%
 

AUrs1
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6
Minimum0
Maximum89
Zeros406
Zeros (%)92.3%
Memory size3.4 KiB
2020-10-27T08:53:50.390155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile73
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.84130296
Coefficient of variation (CV)3.473550493
Kurtosis8.557816257
Mean6
Median Absolute Deviation (MAD)0
Skewness3.224041597
Sum2640
Variance434.3599089
MonotocityNot monotonic
2020-10-27T08:53:50.486400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
040692.3%
 
73102.3%
 
72102.3%
 
8940.9%
 
8240.9%
 
8830.7%
 
8610.2%
 
8110.2%
 
7510.2%
 
ValueCountFrequency (%) 
040692.3%
 
72102.3%
 
73102.3%
 
7510.2%
 
8110.2%
 
ValueCountFrequency (%) 
8940.9%
 
8830.7%
 
8610.2%
 
8240.9%
 
8110.2%
 

AUrs2
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
0
439 
73
 
1
ValueCountFrequency (%) 
043999.8%
 
7310.2%
 
2020-10-27T08:53:50.591895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2020-10-27T08:53:50.657917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:53.223141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.002272727
Min length1

AufHi
Real number (ℝ)

Distinct7
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.06818181818
Minimum-1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2020-10-27T08:53:53.321048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile3
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.803536481
Coefficient of variation (CV)-26.45186839
Kurtosis1.919107236
Mean-0.06818181818
Median Absolute Deviation (MAD)0
Skewness1.668012884
Sum-30
Variance3.252743839
MonotocityNot monotonic
2020-10-27T08:53:53.425533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
-134378.0%
 
38619.5%
 
540.9%
 
440.9%
 
910.2%
 
810.2%
 
210.2%
 
ValueCountFrequency (%) 
-134378.0%
 
210.2%
 
38619.5%
 
440.9%
 
540.9%
 
ValueCountFrequency (%) 
910.2%
 
810.2%
 
540.9%
 
440.9%
 
38619.5%
 

Alkoh
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
-1
434 
1
 
6
ValueCountFrequency (%) 
-143498.6%
 
161.4%
 
2020-10-27T08:53:53.533605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:53:53.601377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:57.004361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.986363636
Min length1

Char1
Real number (ℝ)

Distinct5
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.4863636364
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2020-10-27T08:53:57.098930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile4
Maximum6
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.639433052
Coefficient of variation (CV)-3.37079693
Kurtosis7.401073036
Mean-0.4863636364
Median Absolute Deviation (MAD)0
Skewness3.002813166
Sum-214
Variance2.687740733
MonotocityNot monotonic
2020-10-27T08:53:57.203491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
-139990.7%
 
4163.6%
 
5133.0%
 
681.8%
 
240.9%
 
ValueCountFrequency (%) 
-139990.7%
 
240.9%
 
4163.6%
 
5133.0%
 
681.8%
 
ValueCountFrequency (%) 
681.8%
 
5133.0%
 
4163.6%
 
240.9%
 
-139990.7%
 

Char2
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
-1
428 
6
 
12
ValueCountFrequency (%) 
-142897.3%
 
6122.7%
 
2020-10-27T08:53:57.326388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:53:57.398119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:54:00.005464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.972727273
Min length1

Bes1
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
-1
382 
6
56 
1
 
2
ValueCountFrequency (%) 
-138286.8%
 
65612.7%
 
120.5%
 
2020-10-27T08:54:00.138791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:54:00.232818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:54:05.333507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.868181818
Min length1

Bes2
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
-1
440 
ValueCountFrequency (%) 
-1440100.0%
 
2020-10-27T08:54:05.459709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:54:05.536572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:54:06.282124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Lich1
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
0
340 
2
80 
1
 
19
-1
 
1
ValueCountFrequency (%) 
034077.3%
 
28018.2%
 
1194.3%
 
-110.2%
 
2020-10-27T08:54:07.531105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2020-10-27T08:54:07.621445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:54:11.912061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.002272727
Min length1

Lich2
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
-1
341 
4
97 
3
 
2
ValueCountFrequency (%) 
-134177.5%
 
49722.0%
 
320.5%
 
2020-10-27T08:54:12.031363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:54:12.120240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:54:15.562677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.775
Min length1

Zust1
Categorical

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
0
335 
1
94 
2
 
9
-1
 
2
ValueCountFrequency (%) 
033576.1%
 
19421.4%
 
292.0%
 
-120.5%
 
2020-10-27T08:54:15.692354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:54:15.781241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:54:20.871897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.004545455
Min length1

Zust2
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
-1
436 
2
 
4
ValueCountFrequency (%) 
-143699.1%
 
240.9%
 
2020-10-27T08:54:20.980348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:54:21.054291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:54:22.145484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.990909091
Min length1

Fstf
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.163636364
Minimum0
Maximum7
Zeros200
Zeros (%)45.5%
Memory size3.4 KiB
2020-10-27T08:54:22.242641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.338256253
Coefficient of variation (CV)1.150063968
Kurtosis1.034484642
Mean1.163636364
Median Absolute Deviation (MAD)1
Skewness1.130546428
Sum512
Variance1.790929799
MonotocityNot monotonic
2020-10-27T08:54:22.347988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
020045.5%
 
212829.1%
 
16514.8%
 
4296.6%
 
392.0%
 
571.6%
 
710.2%
 
610.2%
 
ValueCountFrequency (%) 
020045.5%
 
16514.8%
 
212829.1%
 
392.0%
 
4296.6%
 
ValueCountFrequency (%) 
710.2%
 
610.2%
 
571.6%
 
4296.6%
 
392.0%
 

StrklVu
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
A
440 
ValueCountFrequency (%) 
A440100.0%
 
2020-10-27T08:54:22.469517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:54:22.540739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:54:22.605812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

WoTag
Categorical

Distinct8
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
Fr
82 
Do
71 
Di
70 
Mi
63 
Mo
53 
Other values (3)
101 
ValueCountFrequency (%) 
Fr8218.6%
 
Do7116.1%
 
Di7015.9%
 
Mi6314.3%
 
Mo5312.0%
 
Sa4911.1%
 
So4810.9%
 
40.9%
 
2020-10-27T08:54:22.721076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:54:22.811919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:54:33.127970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.981818182
Min length0

FeiTag
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
-1
430 
1
 
10
ValueCountFrequency (%) 
-143097.7%
 
1102.3%
 
2020-10-27T08:54:33.250054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-27T08:54:33.324480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:54:36.792711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.977272727
Min length1

Month
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.727272727
Minimum0
Maximum11
Zeros30
Zeros (%)6.8%
Memory size3.4 KiB
2020-10-27T08:54:36.896440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.17423882
Coefficient of variation (CV)0.554232175
Kurtosis-0.9475060553
Mean5.727272727
Median Absolute Deviation (MAD)3
Skewness-0.1319361941
Sum2520
Variance10.07579209
MonotocityIncreasing
2020-10-27T08:54:36.999950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
66214.1%
 
75011.4%
 
5429.5%
 
9388.6%
 
2388.6%
 
8368.2%
 
3368.2%
 
10327.3%
 
11306.8%
 
0306.8%
 
Other values (2)4610.5%
 
ValueCountFrequency (%) 
0306.8%
 
1204.5%
 
2388.6%
 
3368.2%
 
4265.9%
 
ValueCountFrequency (%) 
11306.8%
 
10327.3%
 
9388.6%
 
8368.2%
 
75011.4%
 

Interactions

2020-10-27T08:52:13.937216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:14.324875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:14.716222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:15.102506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:15.502930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:15.894437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:16.284828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:16.681579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:17.055739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:17.562317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:17.996469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:18.384954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:18.904706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:19.299149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:19.705123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:20.108445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:20.505388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:20.913804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:21.321957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:22.902877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:22.924622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:23.079584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:23.218833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:23.359700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:23.516523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:23.659880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:23.803167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:23.943887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:24.087370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:24.240878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:24.386669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-27T08:52:24.660979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-27T08:52:54.925034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:55.049360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:55.175597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:55.433736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:55.547735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:55.661157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:55.776453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:55.894411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:56.018922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:56.143959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:56.658197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:56.680250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:56.812190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:56.924192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:57.036990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:57.161283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:57.282206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:57.404231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:57.517873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:57.636111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:57.757364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:57.878569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:57.998597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:58.116135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:58.224782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:58.332450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:58.440574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:58.560307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:58.677652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:59.182501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:59.203319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:59.329298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:59.444267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:59.560013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:59.684638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:52:59.797771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:00.052281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:00.169464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:00.288582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:00.407053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:00.524176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:00.637613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:00.743600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:00.849293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:00.962295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:01.079207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:01.195966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:01.309466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:01.798255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:01.820108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:01.942840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:02.049396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:02.152222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:02.265918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:02.381589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:02.500723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:02.614858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:02.724417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:02.835860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:02.951965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:03.066700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:03.181590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:03.293265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:03.405160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:03.513972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:03.634671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:03.751681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:04.253322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:04.274147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:04.556819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:04.692044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:04.812524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:04.945172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:05.068966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:05.193923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:05.326642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:05.462181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:05.591266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:05.719245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:05.843656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:05.964075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:06.095880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:06.217123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:06.339213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:06.469433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:06.602607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:07.126701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:07.148254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:07.290028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:07.417597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:07.550030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:07.689899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:07.813895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:07.939193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:08.066107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:08.202620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:08.338909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:08.467959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:08.591517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:08.713548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:08.985844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:09.117481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:09.239241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:09.368877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:09.502341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:10.010782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:10.031743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:10.158537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:10.273630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:10.392036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:10.522227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:10.640564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:10.754438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:10.874356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:10.992322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:11.121298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:11.247494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:11.369888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:11.485263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:11.596453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:11.714060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:11.828909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:11.960390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:12.087967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-27T08:54:37.496350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-27T08:54:37.931840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-27T08:54:38.375579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-27T08:54:38.817749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-27T08:54:38.885027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-27T08:53:13.049523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-27T08:53:13.765043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTempGLSpatGLTempILSpatILTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfStrklVuWoTagFeiTagMonth
003602226568431100613231171471802132-100-1-1-1-1-1-10-11-10ADi10
17216961299156151104312-12164767003733-100-1-1-1-1-1-1241-10ADo-10
211241910902993700783232130263417727-100-1-1-1-1-1-10-11-10ASa-10
3152885335383650600173232126957927323-100-1-1-1-1-1-10-11-10ASa-10
41613245963126322202512-12109868327119-172733-1-1-1-1-10-1121ASa-10
5183334734534427600123232120496103622-100-1-1-1-16-10-11-10ASo10
625781168078290073232172286833622-100-1-1-1-1-1-10-10-10ADi-10
7261595320224632900313232172353207118-1003-1-1-1-1-1241-12AMi-10
83032713633961147850040323211916262711897203-1-1-1-1-124121AMi-10
939211076712798203652-12140164327118-17204-1-1-1-1-1242-12AFr-10

Last rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTempGLSpatGLTempILSpatILTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfStrklVuWoTagFeiTagMonth
430181939911024780675600273232103183703632-100-1-1-1-1-1-1240-12AMi-111
431182081577117465900653231155061467622-100-1-1-1-1-1-10-10-12AMi-111
432182245314721361834300383232118651107623-100-1-1-1-1-1-1240-10ADo-111
4331831267105109703448403112-121191703113129-1003-1-1-1-1-10-10-10AFr-111
434183272927536911802000213231178256707623-100-1-1-1-1-1-10-11-10ASa-111
43518482414290118101605312-12104599987622-100-1-1-1-1-1-1241-10AMi111
436185447712538041544400143232127461903622-100-1-1-1-16-1240-10AFr-111
43718582375229229630500213232126978423632-100-1-1-1-1-1-10-10-17AFr-111
438185956494432447047001632321785638631429003-1-1-1-1-10-10-10A-111
4391860201931499943721602912-12187557963622-100-1-1-1-1-1-10-10-12ASa-111